Design and analysis of a vibration energy harvester using push-pull electrostatic conversion. (1st September 2020)

• Record Type: Journal Article
• Authors: Erturun, Ugur; Eisape, Adebayo; West, James E
• Is Part Of: Smart materials and structures. Volume 29:Number 10(2020)
• Publication Date: 2020-09-01
• Subjects: energy harvesting -- electrostatic -- electret -- vibrations -- low frequency -- modeling; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/aba5e2
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: Methods of electrostatic conversion are available for harvesting energy where there are ambient vibrations. However, most of the previous work in the literature has addressed applications with high frequencies. In this study, we are not only implementing an electret-based energy harvester for low-frequency applications but also evaluating the effect of parameters, including vibration rates, accelerations, electret surface potential, e.g. on the efficiency of electrostatic energy harvesting (EH). A prototype system, with the size of 4 × 28 cm 3, was built and constructed to accomplish experimental analysis, and the corona triode process was used to prepare electrets by charging Teflon FEP films. In the electret surface potential range of 300–1800 V, vibration frequency range of 2–45 Hz, and acceleration range of 0.1–1.0 g, the effect of parameters on the EH efficiency was experimentally tested. To predict and maximize the performance of the system, a mathematical response surface model (RSM), validated experimentally < 9.5% error. The maximum peak-peak voltage output of 318 V was predicted using this model for the electret surface potential of −1800 V, and vibration frequency of 16 Hz. Moreover, harvested energy was ∼ 900 μ J (∼0.8 µ J per mechanical cycle) in a minute though low frequencies (<20 Hz), which can be easily enhanced to more than 1 mJ with system optimization. We suggest our device can be used in numerous low-frequency applications, and our predictive model can also be used to optimize the efficiency of other electrostatic energy harvesters based on electrets.